Notebook: Modélisation GAMMs des diphtongues descendantes (/ai/, /au/, /ei/, /ou/) en mandarin standard
Chenyu LI
2022-02-22
This notebook is for the acoustic analysis of the falling diphthongs in the standard Mandarin with the approach GAMMs.
#install.packages('rmarkdown')# Importation des emballages
#install.packages("itsadug")
library(ggplot2)
library(mgcv)## Loading required package: nlme## This is mgcv 1.8-33. For overview type 'help("mgcv-package")'.library(itsadug)## Loading required package: plotfunctions##
## Attaching package: 'plotfunctions'## The following object is masked from 'package:ggplot2':
##
## alpha## Loaded package itsadug 2.4 (see 'help("itsadug")' ).source("gamm_hacks.r")
#install.packages("tidyverse")
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ tibble 3.1.0 ✓ dplyr 1.0.5
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1
## ✓ purrr 0.3.4## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x plotfunctions::alpha() masks ggplot2::alpha()
## x dplyr::collapse() masks nlme::collapse()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()After the importation of the packages, let’s read the data.
# Importation des données
au <- read.table(file="au0b.tsv",sep="\t", fileEncoding = 'UTF-16', header = TRUE)
ai <- read.table(file="ai0a.tsv",sep="\t", fileEncoding = 'UTF-16', header = TRUE)
ei <- read.table(file="ei0a.tsv",sep="\t", fileEncoding = 'UTF-16', header = TRUE)
ou <- read.table(file="ou0a.tsv",sep="\t", fileEncoding = 'UTF-16', header = TRUE)Change of the nature of the variables in the dataset.
The criterion is that all the numerical variables are numerated and the string varibles are factored.
1 /ai/
Lets start from /ai/:
ai$sexe<-as.factor(ai$sexe)
ai$ton<-as.factor(ai$ton)
ai$pow<-as.factor(ai$pow)
ai$contexte.D<-as.factor(ai$contexte.D)
ai$contexte.G<-as.factor(ai$contexte.G)
ai$f1<-as.numeric(ai$f1)## Warning: NAs introduced by coercionai$f2<-as.numeric(ai$f2)## Warning: NAs introduced by coercionai$f3<-as.numeric(ai$f3)## Warning: NAs introduced by coercionai$f0<-as.numeric(ai$f0)## Warning: NAs introduced by coercionhead(ai)## numero sexe locuteur diphtongue ton pow contexte.G contexte.D duree.ms.
## 1 1 F FS11 ai 4 f h 0 102.6625
## 2 1 F FS11 ai 4 f h 0 102.6625
## 3 1 F FS11 ai 4 f h 0 102.6625
## 4 1 F FS11 ai 4 f h 0 102.6625
## 5 1 F FS11 ai 4 f h 0 102.6625
## 6 1 F FS11 ai 4 f h 0 102.6625
## measurement.no f1 f2 f3 f0
## 1 0 770.9403 1592.367 2791.365 242.7606
## 2 1 789.5770 1654.538 2661.433 232.8865
## 3 2 790.5264 1676.141 2643.341 228.2137
## 4 3 792.7979 1771.876 2587.896 224.4104
## 5 4 786.4961 1814.919 2436.698 219.7656
## 6 5 760.0966 1827.338 2542.548 214.1222In the dataset we can see the number of the data numero, the gender sexe, the speaker locuteur, the tone ton, the position in the word pow, the context before and after this diphthong contexte.G / contexte.D, the duration of the diphthongs duree.ms. and f0, f1, f2, f3 trajectories, each of them represented by 11 measurements taken at equal intervals (at 0%, 10%, 20%, . . . , 100%).
# Regroupement par les facteurs
ai.mas <- droplevels(subset(ai,sexe=="M"))
ai.fem <- droplevels(subset(ai,sexe=="F"))1.1 Masculin
1.1.1 F1
Then the trajectories of f1 in different tones with regard of the sexes and the durations.
ggplot(ai.mas, aes(x=measurement.no, y=f1, group=numero,
alpha=duree.ms.)) +
facet_grid(~ton) + geom_line()## Warning: Removed 28 row(s) containing missing values (geom_path).
ggplot(ai.fem, aes(x=measurement.no, y=f1, group=numero,
alpha=duree.ms.)) +
facet_grid(~ton) + geom_line()## Warning: Removed 25 row(s) containing missing values (geom_path).
Then the first model with a basic smooth of tone 1 and difference smooths.
ai.mas$ton.ord <- as.ordered(ai.mas$ton)
contrasts(ai.mas$ton.ord) <- "contr.treatment"
ai.mas.gam.diff <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.mas, method="ML")
summary.coefs(ai.mas.gam.diff)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 508.92 20.76 24.514 < 2e-16 ***
## ton.ord2 120.43 22.33 5.393 8.47e-08 ***
## ton.ord3 173.89 23.07 7.536 1.01e-13 ***
## ton.ord4 109.93 21.51 5.110 3.81e-07 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 4.434 5.392 5.263 5.67e-05 ***
## s(measurement.no):ton.ord2 1.017 1.032 10.413 0.00111 **
## s(measurement.no):ton.ord3 1.001 1.002 4.728 0.02980 *
## s(measurement.no):ton.ord4 3.570 4.385 7.968 1.47e-06 ***Then the plots of predictions and difference smooth.
plot_smooth(ai.mas.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)## Summary:
## * ton.ord : factor; set to the value(s): 1, 2, 3, 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * NOTE : No random effects in the model to cancel.
## 
plot_diff(ai.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("1","2")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
##
## measurement.no window(s) of significant difference(s):
## 0.000000 - 7.575758plot_diff(ai.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
##
## measurement.no window(s) of significant difference(s):
## 2.323232 - 10.000000plot_diff(ai.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
##
## measurement.no window(s) of significant difference(s):
## 0.000000 - 2.929293
## 6.969697 - 10.000000The model that accounts for the influence of duree.ms. on the trajectories.
ai.mas.gam.dur <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(duree.ms., bs="cr") +
ti(measurement.no, duree.ms.) +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.mas, method="ML")
summary.coefs(ai.mas.gam.dur)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 521.48 20.47 25.471 < 2e-16 ***
## ton.ord2 96.33 22.45 4.291 1.94e-05 ***
## ton.ord3 174.32 22.42 7.777 1.74e-14 ***
## ton.ord4 97.59 21.32 4.578 5.25e-06 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 4.526 5.458 2.689 0.0153 *
## s(duree.ms.) 5.581 6.507 5.202 3.61e-05 ***
## ti(measurement.no,duree.ms.) 7.258 9.082 4.050 3.73e-05 ***
## s(measurement.no):ton.ord2 2.838 3.498 7.788 1.66e-05 ***
## s(measurement.no):ton.ord3 2.279 2.815 3.364 0.0173 *
## s(measurement.no):ton.ord4 4.009 4.879 6.286 1.52e-05 ***The plots with regard the durations.
plot_smooth(ai.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * duree.ms. : numeric predictor; set to the value(s): 170.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * duree.ms. : numeric predictor; set to the value(s): 80.
## * NOTE : No random effects in the model to cancel.
## fvisgam(ai.mas.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(ai.mas$duree.ms., c(0.1, 0.9)))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * duree.ms. : numeric predictor; with 30 values ranging from 80.084553 to 197.994270.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).
The model with regard of f0.
ai.mas.gam.f0 <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.mas, method="ML")
summary.coefs(ai.mas.gam.f0)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 515.84 19.38 26.619 < 2e-16 ***
## ton.ord2 98.96 21.39 4.628 4.21e-06 ***
## ton.ord3 133.59 22.60 5.912 4.69e-09 ***
## ton.ord4 99.55 19.87 5.009 6.49e-07 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 5.043 6.028 3.347 0.002781 **
## s(f0) 2.675 3.219 6.283 0.000272 ***
## ti(measurement.no,f0) 3.877 4.994 2.958 0.011798 *
## s(measurement.no):ton.ord2 3.114 3.839 7.162 2.42e-05 ***
## s(measurement.no):ton.ord3 2.262 2.803 6.325 0.000460 ***
## s(measurement.no):ton.ord4 4.213 5.118 8.474 < 2e-16 ***The plot of such model.
plot_smooth(ai.mas.gam.f0, view="measurement.no", cond=list(f0=100),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 100.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.mas.gam.f0, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 120.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.mas.gam.f0, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 140.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.mas.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 180.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.mas.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 200.
## * NOTE : No random effects in the model to cancel.
## fvisgam(ai.mas.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; with 30 values ranging from 100.125696 to 212.400605.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).
#ai.mas.gam.smooth <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
# s(f0, bs="cr") +
# ti(measurement.no, f0) +
# s(measurement.no, by=ton.ord, bs="cr") +
# s(measurement.no, numero, bs="fs", xt="cr", m=1, k=5),
# data=ai.mas, method="fREML")We now focus on the central portion of the diphthongs, which means the portions without the inluence of the consonant contexts.
ai.central<-droplevels(subset(ai,measurement.no>=2))
ai.central<-droplevels(subset(ai.central,measurement.no<=8))
ai.central.mas <- droplevels(subset(ai.central,sexe=="M"))
ai.central.fem <- droplevels(subset(ai.central,sexe=="F"))ai.central.mas$ton.ord <- as.ordered(ai.central.mas$ton)
contrasts(ai.central.mas$ton.ord) <- "contr.treatment"
ai.central.mas.gam.diff <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.gam.diff)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 472.08 19.51 24.201 < 2e-16 ***
## ton.ord2 171.54 20.96 8.184 1.28e-15 ***
## ton.ord3 225.45 21.68 10.399 < 2e-16 ***
## ton.ord4 187.63 20.19 9.292 < 2e-16 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 2.404 2.924 9.433 1.01e-05 ***
## s(measurement.no):ton.ord2 1.002 1.004 0.147 0.7039
## s(measurement.no):ton.ord3 1.002 1.005 1.309 0.2530
## s(measurement.no):ton.ord4 2.529 3.067 2.551 0.0533 .plot_smooth(ai.central.mas.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)## Summary:
## * ton.ord : factor; set to the value(s): 1, 2, 3, 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * NOTE : No random effects in the model to cancel.
## 
plot_diff(ai.central.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("1","2")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
##
## measurement.no window(s) of significant difference(s):
## 2.000000 - 8.000000plot_diff(ai.central.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
##
## measurement.no window(s) of significant difference(s):
## 2.848485 - 8.000000plot_diff(ai.central.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
##
## measurement.no window(s) of significant difference(s):
## 2.000000 - 3.030303
## 5.818182 - 8.000000ai.central.mas.gam.dur <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(duree.ms., bs="cr",k=6) +
ti(measurement.no, duree.ms.,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.gam.dur)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 483.05 19.43 24.864 < 2e-16 ***
## ton.ord2 153.92 21.26 7.239 1.19e-12 ***
## ton.ord3 225.56 21.26 10.609 < 2e-16 ***
## ton.ord4 175.66 20.20 8.697 < 2e-16 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 2.393 2.910 10.314 3.87e-06 ***
## s(duree.ms.) 4.484 4.852 4.597 0.000297 ***
## ti(measurement.no,duree.ms.) 1.236 1.431 5.669 0.006689 **
## s(measurement.no):ton.ord2 1.000 1.000 0.094 0.759550
## s(measurement.no):ton.ord3 1.000 1.001 1.487 0.223105
## s(measurement.no):ton.ord4 2.587 3.131 2.965 0.029587 *plot_smooth(ai.central.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * duree.ms. : numeric predictor; set to the value(s): 170.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.central.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * duree.ms. : numeric predictor; set to the value(s): 80.
## * NOTE : No random effects in the model to cancel.
## fvisgam(ai.central.mas.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(ai.central.mas$duree.ms., c(0.1, 0.9)))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * duree.ms. : numeric predictor; with 30 values ranging from 80.084553 to 197.994270.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).
ai.central.mas.gam.f0 <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(f0, bs="cr",k=6) +
ti(measurement.no, f0,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.gam.f0)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 480.55 18.43 26.068 < 2e-16 ***
## ton.ord2 159.40 20.21 7.886 1.3e-14 ***
## ton.ord3 182.35 21.24 8.583 < 2e-16 ***
## ton.ord4 178.19 18.85 9.455 < 2e-16 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 2.449 2.975 7.785 5.8e-05 ***
## s(f0) 2.888 3.362 5.997 0.000297 ***
## ti(measurement.no,f0) 1.005 1.010 6.139 0.013357 *
## s(measurement.no):ton.ord2 1.000 1.000 0.233 0.629530
## s(measurement.no):ton.ord3 1.000 1.001 0.006 0.938828
## s(measurement.no):ton.ord4 2.698 3.256 2.878 0.031657 *plot_smooth(ai.central.mas.gam.f0, view="measurement.no", cond=list(f0=100),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 100.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.central.mas.gam.f0, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 120.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.central.mas.gam.f0, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 140.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.central.mas.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 180.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.central.mas.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 200.
## * NOTE : No random effects in the model to cancel.
## fvisgam(ai.central.mas.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; with 30 values ranging from 100.125696 to 212.400605.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).
1.1.2 F2
Now we look at the trajectories of f2 in different tones with regard of the sexes and the durations.
ggplot(ai.mas, aes(x=measurement.no, y=f2, group=numero,
alpha=duree.ms.)) +
facet_grid(~ton) + geom_line()## Warning: Removed 28 row(s) containing missing values (geom_path).
ggplot(ai.fem, aes(x=measurement.no, y=f2, group=numero,
alpha=duree.ms.)) +
facet_grid(~ton) + geom_line()## Warning: Removed 25 row(s) containing missing values (geom_path).
Then we fit the same model with a basic smooth of tone 1 and difference smooths.
ai.mas.f2.gam.diff <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.mas, method="ML")
summary.coefs(ai.mas.f2.gam.diff)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1808.78 25.76 70.213 < 2e-16 ***
## ton.ord2 -190.78 27.71 -6.885 9.73e-12 ***
## ton.ord3 -182.21 28.63 -6.364 2.89e-10 ***
## ton.ord4 -157.18 26.70 -5.887 5.22e-09 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 3.783 4.654 6.929 7.58e-06 ***
## s(measurement.no):ton.ord2 1.000 1.001 0.783 0.376
## s(measurement.no):ton.ord3 1.001 1.001 0.225 0.635
## s(measurement.no):ton.ord4 1.004 1.008 0.360 0.550Now the plots of f2 with different tones.
plot_smooth(ai.mas.f2.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)## Summary:
## * ton.ord : factor; set to the value(s): 1, 2, 3, 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * NOTE : No random effects in the model to cancel.
## 
plot_diff(ai.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("1","2")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
##
## measurement.no window(s) of significant difference(s):
## 0.000000 - 10.000000plot_diff(ai.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
##
## measurement.no window(s) of significant difference(s):
## 0.000000 - 2.020202plot_diff(ai.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
##
## measurement.no window(s) of significant difference(s):
## 5.252525 - 10.000000The model that accounts for the influence of duree.ms. on the trajectories.
ai.mas.f2.gam.dur <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
s(duree.ms., bs="cr") +
ti(measurement.no, duree.ms.) +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.mas, method="ML")
summary.coefs(ai.mas.f2.gam.dur)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1826.36 25.30 72.182 < 2e-16 ***
## ton.ord2 -210.79 27.64 -7.627 5.22e-14 ***
## ton.ord3 -190.56 27.77 -6.861 1.15e-11 ***
## ton.ord4 -178.75 26.32 -6.793 1.81e-11 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 3.893 4.783 8.213 6.26e-07 ***
## s(duree.ms.) 3.903 4.707 5.118 0.000252 ***
## ti(measurement.no,duree.ms.) 2.869 4.012 12.552 < 2e-16 ***
## s(measurement.no):ton.ord2 1.001 1.002 1.332 0.248618
## s(measurement.no):ton.ord3 1.001 1.003 0.351 0.553691
## s(measurement.no):ton.ord4 1.001 1.003 0.038 0.847241The plots with regard the durations.
plot_smooth(ai.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * duree.ms. : numeric predictor; set to the value(s): 170.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * duree.ms. : numeric predictor; set to the value(s): 80.
## * NOTE : No random effects in the model to cancel.
## fvisgam(ai.mas.f2.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(ai.mas$duree.ms., c(0.1, 0.9)))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * duree.ms. : numeric predictor; with 30 values ranging from 80.084553 to 197.994270.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).
ai.mas.f2.gam.f0 <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.mas, method="ML")
summary.coefs(ai.mas.f2.gam.f0)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1809.15 24.68 73.316 < 2e-16 ***
## ton.ord2 -187.20 27.28 -6.862 1.21e-11 ***
## ton.ord3 -201.12 28.74 -6.999 4.80e-12 ***
## ton.ord4 -156.85 25.33 -6.191 8.79e-10 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 4.100 5.036 9.733 < 2e-16 ***
## s(f0) 5.833 6.673 5.023 2.82e-05 ***
## ti(measurement.no,f0) 2.307 2.805 2.708 0.0422 *
## s(measurement.no):ton.ord2 1.001 1.002 0.005 0.9472
## s(measurement.no):ton.ord3 1.001 1.001 0.034 0.8553
## s(measurement.no):ton.ord4 1.004 1.008 0.011 0.9373plot_smooth(ai.mas.f2.gam.f0, view="measurement.no", cond=list(f0=100),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 100.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.mas.f2.gam.f0, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 120.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.mas.f2.gam.f0, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 140.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.mas.f2.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 180.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.mas.f2.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 200.
## * NOTE : No random effects in the model to cancel.
## fvisgam(ai.mas.f2.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; with 30 values ranging from 100.125696 to 212.400605.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).
The central portion:
ai.central.mas.f2.gam.diff <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.f2.gam.diff)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1797.78 28.10 63.974 < 2e-16 ***
## ton.ord2 -169.65 30.20 -5.618 2.77e-08 ***
## ton.ord3 -154.65 31.23 -4.952 9.20e-07 ***
## ton.ord4 -136.24 29.09 -4.683 3.38e-06 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 1.005 1.011 7.229 0.00732 **
## s(measurement.no):ton.ord2 1.001 1.002 0.268 0.60543
## s(measurement.no):ton.ord3 1.001 1.002 0.035 0.85460
## s(measurement.no):ton.ord4 1.001 1.003 0.595 0.44147plot_smooth(ai.central.mas.f2.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)## Summary:
## * ton.ord : factor; set to the value(s): 1, 2, 3, 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * NOTE : No random effects in the model to cancel.
## 
plot_diff(ai.central.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("1","2")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
##
## measurement.no window(s) of significant difference(s):
## 2.000000 - 8.000000plot_diff(ai.central.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
##
## Difference is not significant.plot_diff(ai.central.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
##
## measurement.no window(s) of significant difference(s):
## 6.242424 - 8.000000plot_diff(ai.central.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","4")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
##
## measurement.no window(s) of significant difference(s):
## 3.939394 - 7.454545ai.central.mas.f2.gam.dur <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(duree.ms., bs="cr",k=6) +
ti(measurement.no, duree.ms.,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.f2.gam.dur)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1809.11 27.47 65.867 < 2e-16 ***
## ton.ord2 -178.42 29.78 -5.992 3.30e-09 ***
## ton.ord3 -158.81 30.29 -5.243 2.09e-07 ***
## ton.ord4 -152.09 28.53 -5.331 1.32e-07 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 1.106 1.204 6.368 0.01168 *
## s(duree.ms.) 2.613 3.155 4.432 0.00315 **
## ti(measurement.no,duree.ms.) 3.274 4.240 7.847 2.48e-06 ***
## s(measurement.no):ton.ord2 1.001 1.002 1.433 0.23173
## s(measurement.no):ton.ord3 1.001 1.002 0.023 0.88142
## s(measurement.no):ton.ord4 1.002 1.003 0.342 0.56017plot_smooth(ai.central.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * duree.ms. : numeric predictor; set to the value(s): 170.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.central.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * duree.ms. : numeric predictor; set to the value(s): 80.
## * NOTE : No random effects in the model to cancel.
## fvisgam(ai.central.mas.f2.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(ai.central.mas$duree.ms., c(0.1, 0.9)))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * duree.ms. : numeric predictor; with 30 values ranging from 80.084553 to 197.994270.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).
ai.central.mas.f2.gam.f0 <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(f0, bs="cr",k=6) +
ti(measurement.no, f0,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.f2.gam.f0)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1808.72 25.82 70.057 < 2e-16 ***
## ton.ord2 -183.71 28.33 -6.485 1.74e-10 ***
## ton.ord3 -198.13 29.76 -6.658 5.86e-11 ***
## ton.ord4 -147.99 26.51 -5.582 3.48e-08 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 1.001 1.002 9.506 0.00212 **
## s(f0) 4.105 4.579 7.115 2.97e-06 ***
## ti(measurement.no,f0) 5.049 5.936 5.348 2.37e-05 ***
## s(measurement.no):ton.ord2 1.001 1.001 0.421 0.51700
## s(measurement.no):ton.ord3 1.001 1.001 0.465 0.49561
## s(measurement.no):ton.ord4 1.001 1.001 0.610 0.43499plot_smooth(ai.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=100),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 100.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 120.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 140.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 180.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(ai.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 200.
## * NOTE : No random effects in the model to cancel.
## fvisgam(ai.central.mas.f2.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; with 30 values ranging from 100.125696 to 212.400605.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).
2 /au/
The next diphthong we look at is /au/.
au$sexe<-as.factor(au$sexe)
au$ton<-as.factor(au$ton)
au$pow<-as.factor(au$pow)
au$contexte.D<-as.factor(au$contexte.D)
au$contexte.G<-as.factor(au$contexte.G)
au$f1<-as.numeric(au$f1)## Warning: NAs introduced by coercionau$f2<-as.numeric(au$f2)## Warning: NAs introduced by coercionau$f3<-as.numeric(au$f3)## Warning: NAs introduced by coercionau$f0<-as.numeric(au$f0)## Warning: NAs introduced by coercion# Regroupement par les facteurs
au.mas <- droplevels(subset(au,sexe=="M"))
au.fem <- droplevels(subset(au,sexe=="F"))2.1 Masculin
2.1.1 F1
Then the trajectories of f1 in different tones with regard of the sexes and the durations.
ggplot(au.mas, aes(x=measurement.no, y=f1, group=numero,
alpha=duree.ms.)) +
facet_grid(~ton) + geom_line()## Warning: Removed 28 row(s) containing missing values (geom_path).
ggplot(au.fem, aes(x=measurement.no, y=f1, group=numero,
alpha=duree.ms.)) +
facet_grid(~ton) + geom_line()## Warning: Removed 5 row(s) containing missing values (geom_path).
Then the first model with a basic smooth of tone 1 and difference smooths.
au.mas$ton.ord <- as.ordered(au.mas$ton)
contrasts(ai.mas$ton.ord) <- "contr.treatment"
au.mas.gam.diff <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(measurement.no, by=ton.ord, bs="cr"),
data=au.mas, method="ML")
summary.coefs(au.mas.gam.diff)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 653.579 6.079 107.522 < 2e-16 ***
## ton.ord.L 92.904 10.611 8.755 < 2e-16 ***
## ton.ord.Q -79.485 12.157 -6.538 9.4e-11 ***
## ton.ord.C 43.908 13.528 3.246 0.00121 **
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 5.228 6.297 17.678 < 2e-16 ***
## s(measurement.no):ton.ord2 1.133 1.254 2.295 0.09241 .
## s(measurement.no):ton.ord3 1.002 1.005 0.364 0.54665
## s(measurement.no):ton.ord4 1.016 1.032 8.584 0.00342 **Then the plots of predictions and difference smooth.
plot_smooth(au.mas.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)## Summary:
## * ton.ord : factor; set to the value(s): 1, 2, 3, 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * NOTE : No random effects in the model to cancel.
## 
plot_diff(au.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("1","2")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
##
## measurement.no window(s) of significant difference(s):
## 0.000000 - 10.000000plot_diff(au.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
##
## Difference is not significant.plot_diff(au.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
##
## measurement.no window(s) of significant difference(s):
## 0.000000 - 2.525253
## 6.868687 - 10.000000The model that accounts for the influence of duree.ms. on the trajectories.
au.mas.gam.dur <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(duree.ms., bs="cr") +
ti(measurement.no, duree.ms.) +
s(measurement.no, by=ton.ord, bs="cr"),
data=au.mas, method="ML")
summary.coefs(au.mas.gam.dur)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 650.142 6.024 107.929 < 2e-16 ***
## ton.ord.L 98.438 10.450 9.420 < 2e-16 ***
## ton.ord.Q -72.959 12.090 -6.035 2.16e-09 ***
## ton.ord.C 42.837 13.539 3.164 0.0016 **
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 5.099 6.148 9.388 < 2e-16 ***
## s(duree.ms.) 6.844 7.743 5.934 4.75e-07 ***
## ti(measurement.no,duree.ms.) 5.769 7.821 2.188 0.03051 *
## s(measurement.no):ton.ord2 1.228 1.417 1.818 0.11762
## s(measurement.no):ton.ord3 1.003 1.005 0.390 0.53369
## s(measurement.no):ton.ord4 2.064 2.565 4.619 0.00881 **The plots with regard the durations.
plot_smooth(au.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * duree.ms. : numeric predictor; set to the value(s): 170.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * duree.ms. : numeric predictor; set to the value(s): 80.
## * NOTE : No random effects in the model to cancel.
## fvisgam(au.mas.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(au.mas$duree.ms., c(0.1, 0.9)))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * duree.ms. : numeric predictor; with 30 values ranging from 88.249203 to 188.623093.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).
The model with regard of f0.
au.mas.gam.f0 <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr"),
data=au.mas, method="ML")
summary.coefs(au.mas.gam.f0)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 646.170 5.867 110.142 < 2e-16 ***
## ton.ord.L 66.939 11.132 6.013 2.63e-09 ***
## ton.ord.Q -73.220 12.391 -5.909 4.86e-09 ***
## ton.ord.C 56.950 13.067 4.358 1.46e-05 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 5.303 6.384 17.712 < 2e-16 ***
## s(f0) 6.430 7.259 11.991 < 2e-16 ***
## ti(measurement.no,f0) 1.001 1.001 19.887 9.23e-06 ***
## s(measurement.no):ton.ord2 1.987 2.477 1.399 0.222
## s(measurement.no):ton.ord3 1.001 1.002 1.609 0.205
## s(measurement.no):ton.ord4 1.001 1.002 0.023 0.882The plot of such model.
plot_smooth(au.mas.gam.f0, view="measurement.no", cond=list(f0=100),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 100.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.mas.gam.f0, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 120.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.mas.gam.f0, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 140.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.mas.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 180.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.mas.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 200.
## * NOTE : No random effects in the model to cancel.
## fvisgam(au.mas.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(au.mas$f0, c(0.1, 0.9), na.rm=TRUE))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; with 30 values ranging from 96.765573 to 193.084157.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).
We now focus on the central portion of the diphthongs, which means the portions without the inluence of the consonant contexts.
au.central<-droplevels(subset(au,measurement.no>=2))
au.central<-droplevels(subset(au.central,measurement.no<=8))
au.central.mas <- droplevels(subset(au.central,sexe=="M"))
au.central.fem <- droplevels(subset(au.central,sexe=="F"))au.central.mas$ton.ord <- as.ordered(au.central.mas$ton)
contrasts(au.central.mas$ton.ord) <- "contr.treatment"
au.central.mas.gam.diff <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=au.central.mas, method="ML")
summary.coefs(au.central.mas.gam.diff)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 558.70 13.33 41.901 < 2e-16 ***
## ton.ord2 153.73 22.20 6.926 9.46e-12 ***
## ton.ord3 148.03 15.44 9.588 < 2e-16 ***
## ton.ord4 153.02 14.19 10.781 < 2e-16 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 2.692 3.266 9.376 2.94e-06 ***
## s(measurement.no):ton.ord2 1.003 1.007 2.218 0.1358
## s(measurement.no):ton.ord3 1.001 1.002 2.129 0.1449
## s(measurement.no):ton.ord4 1.000 1.001 5.466 0.0197 *plot_smooth(au.central.mas.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)## Summary:
## * ton.ord : factor; set to the value(s): 1, 2, 3, 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * NOTE : No random effects in the model to cancel.
## 
plot_diff(au.central.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("1","2")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
##
## measurement.no window(s) of significant difference(s):
## 2.000000 - 8.000000plot_diff(au.central.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
##
## Difference is not significant.plot_diff(au.central.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
##
## Difference is not significant.au.central.mas.gam.dur <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(duree.ms., bs="cr",k=6) +
ti(measurement.no, duree.ms.,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=au.central.mas, method="ML")
summary.coefs(au.central.mas.gam.dur)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 553.39 12.75 43.405 < 2e-16 ***
## ton.ord2 149.36 21.42 6.971 7.04e-12 ***
## ton.ord3 144.26 14.73 9.790 < 2e-16 ***
## ton.ord4 163.53 13.64 11.987 < 2e-16 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 2.768 3.350 9.836 1.6e-06 ***
## s(duree.ms.) 4.797 4.975 12.714 < 2e-16 ***
## ti(measurement.no,duree.ms.) 1.342 1.603 7.822 0.00402 **
## s(measurement.no):ton.ord2 1.002 1.003 3.412 0.06477 .
## s(measurement.no):ton.ord3 1.001 1.001 2.348 0.12578
## s(measurement.no):ton.ord4 1.002 1.004 4.081 0.04380 *plot_smooth(au.central.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * duree.ms. : numeric predictor; set to the value(s): 170.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.central.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * duree.ms. : numeric predictor; set to the value(s): 80.
## * NOTE : No random effects in the model to cancel.
## fvisgam(au.central.mas.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(au.central.mas$duree.ms., c(0.1, 0.9)))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * duree.ms. : numeric predictor; with 30 values ranging from 88.249203 to 188.623093.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).
au.central.mas.gam.f0 <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(f0, bs="cr",k=6) +
ti(measurement.no, f0,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=au.central.mas, method="ML")
summary.coefs(au.central.mas.gam.f0)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 567.54 13.32 42.604 < 2e-16 ***
## ton.ord2 144.91 21.04 6.886 1.44e-11 ***
## ton.ord3 110.99 16.53 6.716 4.32e-11 ***
## ton.ord4 126.86 14.31 8.869 < 2e-16 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 2.753 3.334 6.987 8.87e-05 ***
## s(f0) 4.636 4.908 12.260 < 2e-16 ***
## ti(measurement.no,f0) 1.526 1.880 6.984 0.00104 **
## s(measurement.no):ton.ord2 1.104 1.199 0.634 0.52311
## s(measurement.no):ton.ord3 1.001 1.001 0.104 0.74696
## s(measurement.no):ton.ord4 1.000 1.001 0.516 0.47274plot_smooth(au.central.mas.gam.f0, view="measurement.no", cond=list(f0=100),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 100.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.central.mas.gam.f0, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 120.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.central.mas.gam.f0, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 140.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.central.mas.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 180.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.central.mas.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 200.
## * NOTE : No random effects in the model to cancel.
## fvisgam(au.central.mas.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(au.mas$f0, c(0.1, 0.9), na.rm=TRUE))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; with 30 values ranging from 96.765573 to 193.084157.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).
2.1.2 F2
Now we look at the trajectories of f2 in different tones with regard of the sexes and the durations.
ggplot(au.mas, aes(x=measurement.no, y=f2, group=numero,
alpha=duree.ms.)) +
facet_grid(~ton) + geom_line()## Warning: Removed 28 row(s) containing missing values (geom_path).
ggplot(au.fem, aes(x=measurement.no, y=f2, group=numero,
alpha=duree.ms.)) +
facet_grid(~ton) + geom_line()## Warning: Removed 5 row(s) containing missing values (geom_path).
au.mas.f2.gam.diff <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
s(measurement.no, by=ton.ord, bs="cr"),
data=au.mas, method="ML")
summary.coefs(au.mas.f2.gam.diff)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1146.0817 16.3782 69.976 < 2e-16 ***
## ton.ord.L 90.9336 28.5895 3.181 0.00151 **
## ton.ord.Q 43.3800 32.7569 1.324 0.18567
## ton.ord.C -0.6071 36.4515 -0.017 0.98672
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 4.666 5.675 18.874 <2e-16 ***
## s(measurement.no):ton.ord2 1.784 2.221 0.541 0.526
## s(measurement.no):ton.ord3 1.005 1.011 1.026 0.309
## s(measurement.no):ton.ord4 1.003 1.006 0.830 0.362Now the plots of f2 with different tones.
plot_smooth(au.mas.f2.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)## Summary:
## * ton.ord : factor; set to the value(s): 1, 2, 3, 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * NOTE : No random effects in the model to cancel.
## 
plot_diff(au.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("1","2")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
##
## Difference is not significant.plot_diff(au.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
##
## Difference is not significant.plot_diff(au.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
##
## measurement.no window(s) of significant difference(s):
## 3.737374 - 10.000000The model that accounts for the influence of duree.ms. on the trajectories.
au.mas.f2.gam.dur <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
s(duree.ms., bs="cr") +
ti(measurement.no, duree.ms.) +
s(measurement.no, by=ton.ord, bs="cr"),
data=au.mas, method="ML")
summary.coefs(au.mas.f2.gam.dur)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1144.7951 16.5584 69.137 < 2e-16 ***
## ton.ord.L 92.7316 28.8176 3.218 0.00133 **
## ton.ord.Q 44.8780 32.9935 1.360 0.17403
## ton.ord.C -0.9374 36.4501 -0.026 0.97949
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 4.674 5.685 19.246 <2e-16 ***
## s(duree.ms.) 1.002 1.003 0.149 0.700
## ti(measurement.no,duree.ms.) 4.177 5.924 0.988 0.403
## s(measurement.no):ton.ord2 1.491 1.827 0.365 0.696
## s(measurement.no):ton.ord3 1.017 1.033 0.971 0.315
## s(measurement.no):ton.ord4 1.002 1.004 0.825 0.363The plots with regard the durations.
plot_smooth(au.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * duree.ms. : numeric predictor; set to the value(s): 170.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * duree.ms. : numeric predictor; set to the value(s): 80.
## * NOTE : No random effects in the model to cancel.
## fvisgam(au.mas.f2.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(au.mas$duree.ms., c(0.1, 0.9)))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * duree.ms. : numeric predictor; with 30 values ranging from 88.249203 to 188.623093.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).
au.mas.f2.gam.f0 <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr"),
data=au.mas, method="ML")
summary.coefs(au.mas.f2.gam.f0)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1145.1726 16.3532 70.028 < 2e-16 ***
## ton.ord.L 79.9037 30.4263 2.626 0.00878 **
## ton.ord.Q 30.5677 34.3775 0.889 0.37414
## ton.ord.C 0.3648 36.1553 0.010 0.99195
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 4.420 5.403 16.115 < 2e-16 ***
## s(f0) 1.000 1.000 0.022 0.88388
## ti(measurement.no,f0) 1.001 1.002 8.948 0.00285 **
## s(measurement.no):ton.ord2 2.161 2.690 1.530 0.28340
## s(measurement.no):ton.ord3 1.001 1.002 2.489 0.11474
## s(measurement.no):ton.ord4 1.001 1.002 0.204 0.65200plot_smooth(au.mas.f2.gam.f0, view="measurement.no", cond=list(f0=100),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 100.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.mas.f2.gam.f0, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 120.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.mas.f2.gam.f0, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 140.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.mas.f2.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 180.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.mas.f2.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; set to the value(s): 200.
## * NOTE : No random effects in the model to cancel.
## fvisgam(au.mas.f2.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(au.mas$f0, c(0.1, 0.9), na.rm=TRUE))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
## * f0 : numeric predictor; with 30 values ranging from 96.765573 to 193.084157.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).
The central portion:
au.central.mas.f2.gam.diff <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=au.central.mas, method="ML")
summary.coefs(au.central.mas.f2.gam.diff)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1021.70 38.86 26.290 <2e-16 ***
## ton.ord2 -41.61 64.69 -0.643 0.5203
## ton.ord3 70.25 45.00 1.561 0.1189
## ton.ord4 137.90 41.37 3.334 0.0009 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 1.745 2.121 1.417 0.2323
## s(measurement.no):ton.ord2 1.001 1.002 0.020 0.8896
## s(measurement.no):ton.ord3 1.002 1.005 0.757 0.3836
## s(measurement.no):ton.ord4 1.893 2.307 2.377 0.0804 .plot_smooth(au.central.mas.f2.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)## Summary:
## * ton.ord : factor; set to the value(s): 1, 2, 3, 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * NOTE : No random effects in the model to cancel.
## 
plot_diff(au.central.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("1","2")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
##
## Difference is not significant.plot_diff(au.central.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
##
## measurement.no window(s) of significant difference(s):
## 4.969697 - 7.272727plot_diff(au.central.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
##
## measurement.no window(s) of significant difference(s):
## 6.000000 - 8.000000plot_diff(au.central.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","4")))## Summary:
## * measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
##
## measurement.no window(s) of significant difference(s):
## 4.060606 - 8.000000au.central.mas.f2.gam.dur <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(duree.ms., bs="cr",k=6) +
ti(measurement.no, duree.ms.,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=au.central.mas, method="ML")
summary.coefs(au.central.mas.f2.gam.dur)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1023.68 38.89 26.324 < 2e-16 ***
## ton.ord2 -40.19 64.86 -0.620 0.53563
## ton.ord3 69.89 44.92 1.556 0.12012
## ton.ord4 134.96 41.55 3.248 0.00121 **plot_smooth(au.central.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * duree.ms. : numeric predictor; set to the value(s): 170.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.central.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * duree.ms. : numeric predictor; set to the value(s): 80.
## * NOTE : No random effects in the model to cancel.
## fvisgam(au.central.mas.f2.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(au.central.mas$duree.ms., c(0.1, 0.9)))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * duree.ms. : numeric predictor; with 30 values ranging from 88.249203 to 188.623093.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).
au.central.mas.f2.gam.f0 <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(f0, bs="cr",k=6) +
ti(measurement.no, f0,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=au.central.mas, method="ML")
summary.coefs(au.central.mas.f2.gam.f0)## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1011.64 37.07 27.290 < 2e-16 ***
## ton.ord2 -29.61 58.83 -0.503 0.614896
## ton.ord3 88.26 45.33 1.947 0.052013 .
## ton.ord4 138.85 39.62 3.505 0.000491 ***
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(measurement.no) 1.119 1.210 0.434 0.4770
## s(f0) 1.000 1.000 0.357 0.5506
## ti(measurement.no,f0) 1.002 1.004 1.509 0.2199
## s(measurement.no):ton.ord2 1.000 1.000 0.206 0.6501
## s(measurement.no):ton.ord3 1.001 1.001 0.490 0.4838
## s(measurement.no):ton.ord4 2.243 2.742 3.078 0.0267 *plot_smooth(au.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=100),
rug=F, col="red")## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 100.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 120.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 140.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 180.
## * NOTE : No random effects in the model to cancel.
## plot_smooth(au.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)
## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; set to the value(s): 200.
## * NOTE : No random effects in the model to cancel.
## fvisgam(au.central.mas.f2.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(au.mas$f0, c(0.1, 0.9), na.rm=TRUE))## Summary:
## * ton.ord : factor; set to the value(s): 4.
## * measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
## * f0 : numeric predictor; with 30 values ranging from 96.765573 to 193.084157.
## * NOTE : No random effects in the model to cancel.
## ## Warning in gradientLegend(c(min.z, max.z), n.seg = 3, pos = 0.875, color =
## pal, : Increase right margin to fit labels or decrease the number of decimals,
## see help(gradientLegend).